# NOTE Edit config.py to get num_recs=None, else you'll be restricted to ≤200 recs/sp
#   - Want: countries_k='na', com_names_k='us', num_recs=None,
#   - Not:  countries_k='na', com_names_k='us', num_recs=200,

from notebooks import *
sg.init(None)  # Computes search_recs, if cache miss

search_recs = sg.search_recs

# Utils

def _recs_show(
    df,
    scale=None,
    order=[
        'xc_id', 'spectro_disp',
        'species', 'subspecies',
        'year', 'month_day', 'time',
        'type',
        'state', 'elevation', 'lat', 'lng', 'place',
        'remarks',
        'quality', 'recordist', 'background_species', 'date',
    ],
    drop=['license', 'bird_seen', 'playback_used'],
    astype={'year': int},
    replace={'subspecies': {'': '—'}},
    **kwargs,
):
    return (df
        # Drop any indexes (e.g. from filter/sort)
        .reset_index(drop=True)
        # Featurize
        .pipe(recs_featurize_spectro_disp, scale=scale)
        # View
        .pipe(recs_view_cols, append=order)  # append= to ensure all requested cols are included
        .pipe(df_reorder_cols, last=order)  # last= so that unknown cols show up loudly in the front
        .drop(columns=drop)
        .astype(astype)
        .replace(replace)
    )

# XXX Very slow to run over all 45k recs, do lazily instead
# recs = (search_recs
#     .pipe(df_inspect, lambda df: (df.shape,))
#     .pipe(_recs_show)
#     .pipe(df_inspect, lambda df: (df.shape,))
# )

---

# Copy selected audio/spectro files to talk dir
for sp, xc_id, desc in [
    ('WREN', 90158, 'male-and-female'),
    ('PSFL', 348155, 'dawn-song'),
    ('PSFL', 348142, 'song'),
]:
    payload_id      = 'search_recs-audio_s(10),version(8),com_names_k(us),countries_k(na),num_recs(200)-4a07700'
    search_recs_dir = 'data/cache/payloads/%(payload_id)s/mobile-version(2)/search_recs' % locals()
    talk_dir        = os.path.expanduser('~/Desktop/mb-talk-data')
    shell(**locals(), cmd='''
        cp -v %(search_recs_dir)s/audio/%(sp)s/audio-%(sp)s-%(xc_id)s.mp4     %(talk_dir)s/%(sp)s-%(xc_id)s-%(desc)s.mp4
        cp -v %(search_recs_dir)s/spectro/%(sp)s/spectro-%(sp)s-%(xc_id)s.png %(talk_dir)s/%(sp)s-%(xc_id)s-%(desc)s.png
    ''')

By gender

• WREN
• (More: GHOW, RWBL?)

# WREN
#   - [Pie19] p365
#       - Male song, female song, duet [verify]
#       - [PFGBS] https://academy.allaboutbirds.org/peterson-field-guide-to-bird-sounds/?speciesCode=wrenti
#   - [BNA] https://birdsna.org/Species-Account/bna/species/wrenti/sounds
#       - "A long (2–5 s) series of short, clear, overslurred whistles at about 4 kHz, accelerating into a rapid
#         chatter."
#       - "Female song is similar, but with an even rhythm, and with a highly variable number of notes (3–14)."
#       - "Number of introductory notes is variable, but typically 3–5 in the male song, and the quality of those notes
#         can vary with increasing agitation."
#       - "Partial male songs are common and sound identical to female songs."
#   - Recs
#       - WREN/90158 male and female (duet)
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'WREN',
        # df.remarks.str.lower().str.contains('female'),  # Not useful
        # df.remarks.str.lower().str.contains('slow'),  # Not useful
        # df.remarks.str.lower().str.contains('male'),  # Not useful
        df.xc_id.isin([90158]),
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    .sort_values('date', ascending=False)
    # View
    .pipe(_recs_show,
        scale=3,
    )
    [:500]
)

(52613, 71)

(1, 71)

	xc_id	spectro_disp	species	subspecies	year	month_day	time	type	state	elevation	lat	lng	place	remarks	quality	recordist	background_species	date
0	90158		WREN	—	1999	03-29	?	song	California, United States	40 m	35.3	-121.0	Coon Creek Trail, Montana de Oro State Park, California, United States	Two males and a female or juvenile?	A	Thomas G. Graves	[]	1999-03-29 00:00:00

By age

# RTHA
#   - [Pie19] p205
#       - Squeal: "Given by immature birds in their first summer and fall... May transition gradually into adult Scream
#         over course of first fall and winter; more study needed."
#   - Recs
#       - RTHA/187482 - squeal (juv) - clean
#       - RTHA/139200 - squeal (juv) - messy
#       - RTHA/65409  - scream (adult) - clean
#       - RTHA/173877 - transitional squeal/scream [good]
#       - RTHA/173880 - transitional squeal/scream [good, but too quiet]
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'RTHA',
        df.xc_id.isin([187482, 139200, 65409, 173877, 173880]),
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    # .sample(frac=1, random_state=0)
    # [lambda df: ~df.subspecies.isin([''])].sort_values(['subspecies'], ascending=[True])
    # .sort_values('month_day')
    # View
    .pipe(_recs_show,
        scale=3,
    )
    # [:100]
)

(52613, 71)

(5, 71)

	xc_id	spectro_disp	species	subspecies	year	month_day	time	type	state	elevation	lat	lng	place	remarks	quality	recordist	background_species	date
0	173877		RTHA	—	2012	06-20	09:35	flight call	Florida, United States	20 m	27.9	-81.2	Joe Overstreet Rd, Lake Kissimmee, Osceola County, Florida, United States		B	Paul Marvin	['EAME', 'NOBO', 'COYE']	2012-06-20 00:00:00
1	173880		RTHA	—	2012	10-28	13:29	call	California, United States	10 m	32.6	-117.0	Dairymart Ponds, San Diego County, California, United States	on telephone pole	B	Paul Marvin	[]	2012-10-28 00:00:00
2	65409		RTHA	calurus	2010	06-14	07:45	keer call	California, United States	1520 m	39.7	-120.0	Carman Valley, Sierra County, California, United States	bird calling while perched atop tall conifer. also can hear Douglas's squirrel twittering	A	Eric DeFonso	['MODO', 'AMRO']	2010-06-14 00:00:00
3	187482		RTHA	—	2014	07-16	10:30	call, juvenile	British Columbia, Canada	90 m	48.5	-123.0	Hector Rd, Vancouver Island, British Columbia, Canada	Whistles from recently-fledged juvenile, given in flight.	no score	Ian Cruickshank	[]	2014-07-16 00:00:00
4	139200		RTHA	borealis	2013	06-18	16:30	call, juvenile	Georgia, United States	3 m	31.9	-81.1	Savannah, Chatham, Georgia, United States	Amplification was boosted. The original recording was shortened to exclude nearby construction noise. The intervals between the calls are as recorded. The two fledglings were following a parent ...	A	Russ Wigh	['BTGR']	2013-06-18 00:00:00

By time of day

# PSFL: dawn song vs. normal song
#   - [Pie19] p272
#       - Dawn song, normal song
#       - (No "dawn" at [PFGBS] https://academy.allaboutbirds.org/peterson-field-guide-to-bird-sounds/?speciesCode=pasfly)
#   - [BNA] https://birdsna.org/Species-Account/bna/species/pasfly/sounds
#       - Same as [Pie19]
#   - Recs
#       - PSFL/348155 dawn song
#       - PSFL/348142 song
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'PSFL',
        df.recordist.str.lower().str.match(r'.*webster.*'),
        df.xc_id.isin([348155, 348142]),
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    # .sort_values(['subspecies', 'lat'], ascending=[True, False])
    # .sort_values(['lat'], ascending=False)
    # .sort_values(['lng'], ascending=True)
    .sort_values(['time'], ascending=True)
    # View
    .pipe(_recs_show,
        scale=3,
    )
    [:500]
)

(52613, 71)

(2, 71)

	xc_id	spectro_disp	species	subspecies	year	month_day	time	type	state	elevation	lat	lng	place	remarks	quality	recordist	background_species	date
0	348155		PSFL	cineritius	2001	06-15	05:20	dawn song	Baja California, Mexico	2300 m	31.0	-116.0	La Corona Arriba, Sierra San Pedro Martir, Baja California, Mexico	territory No 1, 3rd recording; some interaction type notes toward the end	B	Richard E. Webster	['MOCH', 'ACWO']	2001-06-15 00:00:00
1	348142		PSFL	cineritius	2001	06-15	05:25	male position note	Baja California, Mexico	2300 m	31.0	-116.0	La Corona Arriba, Sierra San Pedro Martir, Baja California, Mexico	territory No 1 recorded here, 4th recording; male position note	A	Richard E. Webster	['MOCH', 'DEJU', 'WBNU', 'PYNU']	2001-06-15 00:00:00

Within one individual

# HUVI
#   - [Pie19] p297
#       - "Phrases vary tremendously"
#       - "Incessant repetition of the same sound can be an excellent field mark, but singing occasionally deviates from
#         this pattern"
#   - [BNA] https://birdsna.org/Species-Account/bna/species/hutvir/sounds
#       - "Much individual and geographic variation"
#       - "Typical song pattern is a monotonous and unmusical series of nasal and wheezy 2-syllable, ascending phrases
#         ... repeated approximately 1 per second for a period sometimes spanning ≥ 10 min"
#       - "A common variation is composed of descending phrases"
#       - "A bird may sing either form, may alternate between the 2 forms, and occasionally may mix ascending and
#         descending phrases in the same series ... producing a pattern reminiscent of the song of Cassin's Vireo"
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'HUVI',
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    .sample(frac=1, random_state=2)
    # View
    .pipe(_recs_show,
        scale=3,
    )
    [:25]
)

(52613, 71)

(179, 71)

	xc_id	spectro_disp	species	subspecies	year	month_day	time	type	state	elevation	lat	lng	place	remarks	quality	recordist	background_species	date
0	402932		HUVI	—	2018	02-14	08:19	call	Arizona, United States	1800 m	32.4	-111.0	Cypress Picnic Area, Mount Lemmon, Pima County, Arizona, United States	Hutton's vireo calls recorded at a range of roughly 40 - 50 meters on a north-facing slope with Chihuahua pine, ponderosa pine, alligator juniper, and oaks. Distant roar from a plane is audible....	no score	Lance A. M. Benner	['ACWO']	2018-02-14 00:00:00
1	412615		HUVI	—	2018	04-08	16:42	call	Cerro Alto Campground, United States	340 m	35.4	-121.0	Atascadero, California, Cerro Alto Campground, United States		B	Thomas G. Graves	['STJA']	2018-04-08 00:00:00
2	92953		HUVI	—	2008	04-30	08:55	song	TX, United States	1900 m	30.7	-104.0	N side Mt Livermore, Davis Mts, TX, United States	Natural song (or response to pishing?) from a bird about 3-4m up in Pinyon Pine amid Ponderosa and madrone/oak parkland.	B	Daniel Lane	['WWDO', 'BEWR']	2008-04-30 00:00:00
3	156192		HUVI	—	2011	06-10	15:00	song	British Columbia, Canada	420 m	48.5	-124.0	Crabapple Lake, Sooke Hills, Vancouver Island, British Columbia, Canada		A	Ian Cruickshank	['AMRO', 'TOWA']	2011-06-10 00:00:00
4	183585		HUVI	—	2014	06-24	07:00	song	British Columbia, Canada	80 m	48.5	-123.0	Saanich (near Victoria), Capital, British Columbia, Canada	somewhat atypical song (fast phrasing).	B	Ian Cruickshank	[]	2014-06-24 00:00:00
5	405621		HUVI	—	2018	03-11	14:05	song	California, United States	1300 m	34.2	-118.0	Glendora Ridge Road mile 10.4, San Gabriel Mountains, Los Angeles County, California, United States	Recorded at a range of about 70 meters on a forested, north-facing slope. Equipment: Sony PCM-M10 recorder, Sennheiser ME62 microphone, and a Telinga 22 inch parabola. Edits: trimmed and normalize...	no score	Lance A. M. Benner	['OATI']	2018-03-11 00:00:00
6	232139		HUVI	—	2001	03-11	12:00	call?	Oaxaca, Mexico	2600 - 2800 m	17.2	-96.6	Cerro San Felipe (La Cumbre), Oaxaca, Mexico	ID certainty 90%. (Archiv. tape 121 side B track 19 seq. A)	B	Peter Boesman	[]	2001-03-11 00:00:00
7	30595		HUVI	stephensi	2009	02-15	09:00	song	New Mexico, United States	? m	31.4	-109.0	Guadalupe Canyon, Hidalgo Co., New Mexico, United States		A	Andrew Spencer	[]	2009-02-15 00:00:00
8	323414		HUVI	stephensi	2015	05-31	11:15	song	Chiricahua Mountains, United States	1700 m	31.8	-109.0	Rucker Canyon, Chiricahua Mountains, United States	in response to pygmy-owl fuss disturbance; Individual No 2, 2nd recording today	B	Richard E. Webster	['BUSH', 'DCFL', 'BRTI']	2015-05-31 00:00:00
9	125471		HUVI	—	2013	03-17	09:25	song	Cerro Alto Campground, United States	320 m	35.4	-121.0	Atascadero, California, Cerro Alto Campground, United States	Hutton's Vireo again? with a little burr in it's voice? A pair singing.	A	Thomas G. Graves	[]	2013-03-17 00:00:00
10	296985		HUVI	—	2011	06-22	10:56	song	Texas, United States	2000 m	29.3	-103.0	Laguna Meadows Trail, Big Bend National Park, Brewster Co., Texas, United States		B	Paul Marvin	['SCOR']	2011-06-22 00:00:00
11	282857		HUVI	—	2015	03-04	16:30	male, song	Arizona, United States	1000 m	33.9	-111.0	Bushnell Tanks, Sunflower, Maricopa County, Arizona, United States	Amplification, low and high pass filters used in Audacity.	A	Eric Hough	['DEJU', 'BUSH']	2015-03-04 00:00:00
12	252186		HUVI	—	2015	06-10	08:30	song	Mex DF entrance Desierto de los Leones, Mexico	2900 m	19.3	-99.3	Mex DF entrance Desierto de los Leones, Mexico		B	Manuel Grosselet	[]	2015-06-10 00:00:00
13	413985		HUVI	—	2018	05-06	08:00	call	British Columbia, Canada	110 m	48.6	-123.0	Newton Heights, Victoria, British Columbia, Canada	Very quiet calls made while foraging, after one song note. These calls used the same rhythm as a typical song (though a little faster), and were almost inaudible (bird was only about 2m away from m...	C	Randy Dzenkiw	[]	2018-05-06 00:00:00
14	365405		HUVI	mexicanus	2017	03-25	07:00	call	Michoacán, Mexico	2000 m	19.5	-102.0	Uruapan, Michoacán, Mexico	Bosque de pino-encino. Ruido por gallo doméstico, audio amplificado 25dB y filtrado High pass.	no score	Juan Carlos Pérez M.	[]	2017-03-25 00:00:00
15	297611		HUVI	—	2012	07-25	08:44	song	Arizona, United States	1800 m	31.7	-111.0	Super Trail, Madera Canyon, Continental, Arizona, United States		A	Paul Marvin	['GRPE', 'WBNU', 'PLVI']	2012-07-25 00:00:00
16	212822		HUVI	oberholseri	2015	02-07	15:30	call	California, United States	10 m	32.6	-117.0	Tijuana River Valley Regional Park, San Diego County, California, United States	Riparian habitat in arid region near the coast.	C	Nick Komar	[]	2015-02-07 00:00:00
17	282597		HUVI	stephensi	2015	08-14	08:30	call, song	Sonora, Mexico	2100 m	28.4	-109.0	Mesa del Campanero, Sonora, Mexico	High pass filter ran and amplified. In an area of mixed Pine-oak with a lot of Arbutus sp with a Arctostaphylos sp. dominated understory. Came in to spishing and pygmy owl whistling. Part of a lo...	B	David Vander Pluym	[]	2015-08-14 00:00:00
18	105702		HUVI	cognatus	2008	07-15	1100	excited song	Baja California Sur, Mexico	1900 m	23.5	-110.0	Sierra La Laguna Biosphere Reserve, Baja California Sur, Mexico	Solo bird, not one of the two from XC105701	B	Nathan Pieplow	['ACWO', 'SPTO', 'YEJU', 'PSFL', 'WWDO', 'BGGN']	2008-07-15 00:00:00
19	256398		HUVI	—	2014	03-28	?	song	Arizona, United States	140 m	34.3	-114.0	Bill Williams River NWR, Arizona, United States	Documentation for rare but increasing species in this area.	C	Lauren Harter	[]	2014-03-28 00:00:00
20	358938		HUVI	—	2017	03-06	12:00	call	Texas, United States	340 m	29.6	-98.6	Texas (near San Antonio), Bexar County, Texas, United States	one of several calling birds in a close area. volume maximized and cleaned up with a 300 hz highpass filter	B	David Sarkozi	[]	2017-03-06 00:00:00
21	168038		HUVI	—	2014	02-23	08:00	call	Mex DF entrance Desierto de los Leones, Mexico	2900 m	19.3	-99.3	Mex DF entrance Desierto de los Leones, Mexico	perched and moving in the tree, calling.	A	Manuel Grosselet	['BBSO', 'VGSW', 'GRSF']	2014-02-23 00:00:00
22	324079		HUVI	—	2015	05-01	09:29	song	Arizona, United States	1600 m	31.9	-109.0	South Fork Road, Portal, Arizona, United States		B	Paul Marvin	[]	2015-05-01 00:00:00
23	73497		HUVI	—	2011	03-15	08:30	Song	British Columbia, Canada	40 m	48.5	-123.0	Mount Douglas Park, Victoria, British Columbia, Canada		A	Ian Cruickshank	[]	2011-03-15 00:00:00
24	283520		HUVI	stephensi	2015	08-14	12:05	song	Sonora, Mexico	2100 m	28.4	-109.0	Mesa del Campanero, Sonora, Mexico	High pass filter ran and amplified. North edge of mesa, but on top, mixed pine and oak with somewhat open understory, though some patches of denser brush and shorter trees. Had been singing for a w...	B	David Vander Pluym	[]	2015-08-14 00:00:00

By time of year (season)

• Omitted: not worthwhile given the ones we already have above
  • YRWA - visually similar to WIWA, doesn't add much
  • HETH - takes a moment to see
  • RCKI - hard to visually parse

def _show_seasonal(
    species,
    filters=lambda df: [],
    bins=2,  # 2 | 4
    n=None,
    scale=None,
    scale_base=3.8,
    random_state=0,
):
    assert bins in [2, 4]
    if n is None:
        n = 50 // bins
    return (search_recs
        .pipe(df_inspect, lambda df: (df.shape,))
        # Filter
        [lambda df: reduce(lambda x, y: x & y, [
            df.species == species,
            *filters(df),
        ])]
        .pipe(df_inspect, lambda df: (df.shape,))
        # Sort
        # .sample(100, random_state=random_state)
        .sort_values(['month_day'], ascending=[False])
        # View
        .pipe(_recs_show,
            scale=scale or scale_base / bins,
        )
        [:1000]  # Safeguard: trim to a reasonable max (> any sp, but << all recs)
        # Bin by season (4 bins)
        #   - Boundaries based roughly on https://birdsna.org/Species-Account/bna/species/ruckin/breeding
        .assign(season=lambda df: df.month_day.str.split('-').str[0].map(lambda x: {
            '09': 'fall migration',
            '10': 'fall migration',
            '11': 'fall migration',
            '12': 'winter',
            '01': 'winter',
            '02': 'winter',
            '03': 'spring migration',
            '04': 'spring migration',
            '05': 'spring migration',
            '06': 'breeding',
            '07': 'breeding',
            '08': 'breeding',
        }.get(x)))
        .assign(season=lambda df: df.season.pipe(lambda s: s.pipe(as_ordered_cat, [
            'fall migration', 'winter', 'spring migration', 'breeding',
        ])))
        # Bin further
        #   - For bigger spectros (4->2 cols)
        .pipe(lambda df: df if bins == 4 else (df
            .replace({'season': {
                'spring migration': 'spring migration / breeding',
                'breeding':         'spring migration / breeding',
                'fall migration':   'fall migration / winter',
                'winter':           'fall migration / winter',
            }})
            .assign(season=lambda df: df.season.pipe(lambda s: s.pipe(as_ordered_cat, [
                'fall migration / winter', 'spring migration / breeding',
            ])))
        ))
        # Pivot by season (manually)
        .pipe(lambda df: pd.concat(axis=1, objs=[
            (df
                [df.season == season]
                [['spectro_disp']]
                .rename(columns={'spectro_disp': season})
                .sample(frac=1, random_state=random_state)  # Randomize per column (to avoid weird biases from incidental sorting above)
                .reset_index(drop=True)
            )
            for season in df.season.sort_values().unique()
        ]))
        .fillna('')
        .pipe(df_inspect, lambda df: (df.shape,))
        [:n]
        .pipe(df_inspect, lambda df: (df.shape,))
    )

# Good: visually apparent
_show_seasonal('WIWA')

(52613, 71)

(204, 71)

(180, 2)

(25, 2)

	fall migration / winter	spring migration / breeding
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

# Good
#   - Take a moment to explain wraa calls on the left vs. trill songs on the right
_show_seasonal('SPTO')

(52613, 71)

(521, 71)

(429, 2)

(25, 2)

	fall migration / winter	spring migration / breeding
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

# Good
#   - Take a moment to explain chips on the left vs. songs on the right
show_seasonal('FOSP')

(52613, 71)

(501, 71)

(391, 2)

(25, 2)

	fall migration / winter	spring migration / breeding
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

# [Skippable]
# Good example of partial switch
#   - [Pie19] "All year, but mostly Mar—Aug"
_show_seasonal('SOSP')

(52613, 71)

(648, 71)

(498, 2)

(25, 2)

	fall migration / winter	spring migration / breeding
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

By subspecies (/ geography)

def _show_seasonal(
    species,
    filters=lambda df: [],
    bins=2,  # 2 | 4
    n=None,
    scale=None,
    scale_base=3.8,
    random_state=0,
):
    assert bins in [2, 4]
    if n is None:
        n = 50 // bins
    return (search_recs
        .pipe(df_inspect, lambda df: (df.shape,))
        # Filter
        [lambda df: reduce(lambda x, y: x & y, [
            df.species == species,
            *filters(df),
        ])]
        .pipe(df_inspect, lambda df: (df.shape,))
        # Sort
        # .sample(100, random_state=random_state)
        .sort_values(['month_day'], ascending=[False])
        # View
        .pipe(_recs_show,
            scale=scale or scale_base / bins,
        )
        [:1000]  # Safeguard: trim to a reasonable max (> any sp, but << all recs)
        # Bin by season (4 bins)
        #   - Boundaries based roughly on https://birdsna.org/Species-Account/bna/species/ruckin/breeding
        .assign(season=lambda df: df.month_day.str.split('-').str[0].map(lambda x: {
            '09': 'fall migration',
            '10': 'fall migration',
            '11': 'fall migration',
            '12': 'winter',
            '01': 'winter',
            '02': 'winter',
            '03': 'spring migration',
            '04': 'spring migration',
            '05': 'spring migration',
            '06': 'breeding',
            '07': 'breeding',
            '08': 'breeding',
        }.get(x)))
        .assign(season=lambda df: df.season.pipe(lambda s: s.pipe(as_ordered_cat, [
            'fall migration', 'winter', 'spring migration', 'breeding',
        ])))
        # Bin further
        #   - For bigger spectros (4->2 cols)
        .pipe(lambda df: df if bins == 4 else (df
            .replace({'season': {
                'spring migration': 'spring migration / breeding',
                'breeding':         'spring migration / breeding',
                'fall migration':   'fall migration / winter',
                'winter':           'fall migration / winter',
            }})
            .assign(season=lambda df: df.season.pipe(lambda s: s.pipe(as_ordered_cat, [
                'fall migration / winter', 'spring migration / breeding',
            ])))
        ))
        # Pivot by season (manually)
        .pipe(lambda df: pd.concat(axis=1, objs=[
            (df
                [df.season == season]
                [['spectro_disp']]
                .rename(columns={'spectro_disp': season})
                .sample(frac=1, random_state=random_state)  # Randomize per column (to avoid weird biases from incidental sorting above)
                .reset_index(drop=True)
            )
            for season in df.season.sort_values().unique()
        ]))
        .fillna('')
        .pipe(df_inspect, lambda df: (df.shape,))
        [:n]
        .pipe(df_inspect, lambda df: (df.shape,))
    )

# SPTO
#   - [Pie19] p425
#       - "Typical version: 2 unmusical series or trills, second higher"
#       - "West Coast version: single long buzz"
#   - [BNA] https://birdsna.org/Species-Account/bna/species/spotow/sounds
#       - "Interior males sing songs generally containing both introductory and trill phrases, while coastal birds usually
#         sing only trills."
#       - Far western (coastal) birds trill faster on average than interior (mountain, plains) western birds, but trill
#         rates are variable and largely overlapping (15.4–32.3 syll/s vs. 14.5–21.3 syll/s)
#   - [BNA] https://birdsna.org/Species-Account/bna/species/spotow/systematics
#       - 21 subsp (see map)
#       - Various isolated subsp in the southern end of range (MX)
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'SPTO',
        # df.type.str.lower().str.match(r'.*song.*'),
        # df.type.str.lower().str.match(r'^song$'),
        df.quality.isin(['A', 'B']),
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    # .sample(250, random_state=0)
    # .sample(50, random_state=0)
    # .sort_values(['subspecies', 'lat'], ascending=False)
    # .sort_values(['lat'], ascending=False)
    # .sort_values(['lng'], ascending=True)
    .pipe(df_inspect, lambda df: (df.shape,))
    # View
    .pipe(_recs_show,
        scale=1.9,
    )

    # Bin by geo (2 bins)
    #   - Very rough, but decent results

    # .assign(geo=lambda df: df.state.str.lower().str.replace('.', '').str.split(r',\s*').map(lambda states: first(
    #     [
    #         geo
    #         for state in states
    #         for geo in [{
    #
    #             # # 'british columbia': 'west coast',
    #             # # 'bc':               'west coast',
    #             # # 'washington':       'west coast',
    #             # # 'wa':               'west coast',
    #             # # 'oregon':           'west coast',
    #             # 'california':       'west coast',
    #             # 'ca':               'west coast',
    #             # # 'inyo county':      'interior',  # Right? (Inyo County ~ Death Valley, CA)
    #             # # 'colorado':         'interior',
    #             # # 'utah':             'interior',
    #             # 'arizona':          'interior',
    #             # 'az':               'interior',
    #             # 'new mexico':       'interior',
    #             # # 'mexico':           'interior',
    #
    #             # 'british columbia': 'west coast',
    #             # 'bc':               'west coast',
    #             # 'washington':       'west coast',
    #             # 'wa':               'west coast',
    #             # 'oregon':           'west coast',
    #             'california':       'california',
    #             'ca':               'california',
    #             # 'inyo county':      'interior',  # Right? (Inyo County ~ Death Valley, CA)
    #             'colorado':         'colorado',
    #             # 'utah':             'interior',
    #             # 'arizona':          'arizona',
    #             # 'az':               'arizona',
    #             # # 'new mexico':       'new mexico',
    #             # 'mexico':           'mexico',
    #
    #         }.get(state)]
    #         if geo is not None
    #     ] + [
    #         None,
    #     ]
    # )))

    .assign(geo=lambda df: df.pipe(df_map_rows, lambda row: (
        'W -117 (coastal CA)' if row.lng < -117 else  # -117 to exclude Inyo County (Death Valley) which behaves ~like interior
        'E -110 (CO/AZ/MX)'   if row.lng > -110 else  # -110 to include CO/AZ/MX (at least)
        None                                          # Throw out everything in the middle, to simplify analysis
    )))

    [lambda df: df.geo.notnull()]
    .assign(geo=lambda df: df.geo.pipe(lambda s: s.pipe(as_ordered_cat, [
        # 'west coast', 'interior',
        # 'california', 'colorado', 'arizona', 'mexico',
        # 'california', 'colorado',
        'W -117 (coastal CA)', 'E -110 (CO/AZ/MX)',
    ])))
    # .pipe(df_inspect, lambda df: df[:250])  # Debug

    # Pivot by geo (manually)
    .pipe(lambda df: pd.concat(axis=1, objs=[
        (df
            [df.geo == geo]
            [['spectro_disp']]
            .rename(columns={'spectro_disp': geo})
            .sample(frac=1, random_state=0)  # Randomize per column (to avoid weird biases from incidental sorting above)
            .reset_index(drop=True)
        )
        for geo in df.geo.sort_values().unique()
    ]))
    .fillna('')
    .pipe(df_inspect, lambda df: (df.shape,))
    [:25]
    .pipe(df_inspect, lambda df: (df.shape,))

)

(52613, 71)

(432, 71)

(432, 71)

(230, 2)

(25, 2)

	W -117 (coastal CA)	E -110 (CO/AZ/MX)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24

Final quiz

• (All the damn wraa calls)

# To search for examples
# (search_recs
#     .pipe(df_inspect, lambda df: (df.shape,))
#     # Filter
#     [lambda df: reduce(lambda x, y: x & y, [
#         df.species == 'GGSH',
#     ])]
#     .pipe(df_inspect, lambda df: (df.shape,))
#     # Sort
#     .sample(frac=1, random_state=0)
#     # View
#     .pipe(_recs_show,
#         # scale=3,
#     )
#     [:250]
# )

(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x | y, [

        (df.species == 'SPTO') & df.xc_id.isin([127012]),
        (df.species == 'EATO') & df.xc_id.isin([293823]),

        (df.species == 'HUVI') & df.xc_id.isin([297120]),
        # (df.species == 'HUVI') & df.xc_id.isin([348987]),
        (df.species == 'WAVI') & df.xc_id.isin([159366]),
        # (df.species == 'WAVI') & df.xc_id.isin([381527]),

        (df.species == 'HETH') & df.xc_id.isin([314303]),
        # (df.species == 'HETH') & df.xc_id.isin([131636]),

        (df.species == 'BEWR') & df.xc_id.isin([163209]),
        # (df.species == 'BEWR') & df.xc_id.isin([141349]),
        (df.species == 'HOWR') & df.xc_id.isin([265810]),

        (df.species == 'BANO') & df.xc_id.isin([294969]),  # Juv shriek [PFGBS]
        (df.species == 'GHOW') & df.xc_id.isin([154990]),  # Juv shriek [PFGBS]

        (df.species == 'BGGN') & df.xc_id.isin([376229]),
        # (df.species == 'BGGN') & df.xc_id.isin([81059]),
        (df.species == 'BCGN') & df.xc_id.isin([30087]),
        (df.species == 'BTGN') & df.xc_id.isin([253889]),
        (df.species == 'CAGN') & df.xc_id.isin([17808]),

        (df.species == 'LOSH') & df.xc_id.isin([255158]),
        # (df.species == 'LOSH') & df.xc_id.isin([255145]),
        (df.species == 'GGSH') & df.xc_id.isin([91968]),  # NOSH (Northern Shrike) used to be GGSH (Great Gray Shrike)

        (df.species == 'CASJ') & df.xc_id.isin([347904]),
        (df.species == 'STJA') & df.xc_id.isin([146610]),

    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # View
    .pipe(_recs_show,
        scale=2.9,
    )
    .pipe(df_ordered_cats_like, species=metadata.ebird.df.shorthand)
    .sort_values(['species'])
)

(52613, 71)

(17, 71)

	xc_id	spectro_disp	species	subspecies	year	month_day	time	type	state	elevation	lat	lng	place	remarks	quality	recordist	background_species	date
4	294969		BANO	—	2015	06-06	04:30	, begging call, cleaking, juvenile	Washington, United States	100 m	47.7	-122.0	Seattle, King County, Washington, United States	The recording starts with a young juvenile begging (with another juvenile in the background) at :10 an adult and does a food drop at :43 the adult starts vocalizing, eventually both the juvenile an...	A	Bruce Lagerquist	['AMRO', 'KILL']	2015-06-06 00:00:00
1	154990		GHOW	—	2012	10-22	05:30	call	Pennsylvania, United States	40 m	40.1	-75.1	Lower Moreland (near Huntingdon Valley), Montgomery, Pennsylvania, United States	shrieks and chitter call	A	Paul Driver	[]	2012-10-22 00:00:00
11	255158		LOSH	—	2014	08-29	10:25	call	Arizona, United States	1100 m	31.3	-109.0	San Bernardino National Wildlife Refuge, Cochise County, Arizona, United States	wintering or migrating bird at this location	A	Richard E. Webster	[]	2014-08-29 00:00:00
10	91968		GGSH	invictus	2011	12-22	09:00	call, song	Washington, United States	550 m	49.0	-119.0	Mary Ann Creek Road, Okanogan Co., Washington, United States	from an adult bird a few minutes after playback. Two different call types and some song in this cut.	A	Andrew Spencer	[]	2011-12-22 00:00:00
5	297120		HUVI	—	2012	12-09	07:12	call	California, United States	10 m	32.6	-117.0	Bird and Butterfly Garden, Tijuana River Valley Regional Park, San Diego Co, California, United States		A	Paul Marvin	[]	2012-12-09 00:00:00
9	159366		WAVI	—	2012	08-26	07:44	call	BC, Canada	m	48.5	-123.0	Swan Lake, Victoria, Vancouver Island, BC, Canada	Recorded on a Marantz PMD661 recorder using a Sennheiser ME66 microphone.	B	Ian Cruickshank	[]	2012-08-26 00:00:00
12	146610		STJA	—	2013	08-24	17:30	call	Arizona, United States	2100 m	35.2	-112.0	Flagstaff, Coconino, Arizona, United States	Natural calls from a bird about 5m up, dislodging cones from a branch and watching them fall to the ground in Ponderosa Pine parkland.	B	Dan Lane	['LEGO', 'PYNU']	2013-08-24 00:00:00
8	347904		CASJ	—	2016	12-25	17:00	call	California, United States	10 m	38.6	-121.0	American River Parkway (East of Watt), Sacramento, Sacramento County, California, United States		A	Jim Holmes	[]	2016-12-25 00:00:00
3	265810		HOWR	cahooni	2015	05-26	19:00	call	Jalisco, Mexico	2500 m	20.7	-105.0	Cerro La Bufa, San Sebastián del Oeste, Jalisco, Mexico	calls from the other member of the pair from XC265809 (so a presumed female).	A	Andrew Spencer	[]	2015-05-26 00:00:00
15	163209		BEWR	—	2014	01-04	12:24	alarm call	Little Spokane River Natural Area, United States	700 m	47.8	-118.0	Indian Painted Rocks, Little Spokane River Natural Area, United States		A	Garrett MacDonald	[]	2014-01-04 00:00:00
13	376229		BGGN	—	2017	06-18	05:12	call	Colorado, United States	1900 m	39.9	-105.0	Colorado (near Boulder), Boulder County, Colorado, United States	A Blue-gray Gnatcatcher going off from a shrub along the Fowler Trail.	A	Ted Floyd	[]	2017-06-18 00:00:00
7	17808		CAGN	—	2008	01-03	14:30	call	California, United States	30 m	33.6	-118.0	Crystal Cove State Park, Orange County, California, United States		B	Nathan Pieplow	['Homo sapiens']	2008-01-03 00:00:00
0	253889		BTGN	—	2015	04-20	05:47	song	Arizona, United States	600 m	33.5	-112.0	Mesa, Maricopa County, Arizona, United States	Recorded with Sennheiser ME64 /K6 microphone on a Olympus LS-14 recorder. Bird calling from Palo Verde tree. Bird reacted to its own (previous) call from my recorder speaker when I wanted to chec...	A	Patrick Turgeon	[]	2015-04-20 00:00:00
16	30087		BCGN	—	2009	02-15	11:40	call	New Mexico, United States	? m	31.4	-109.0	Guadalupe Canyon, Hidalgo Co., New Mexico, United States	calls from a male bird, part of a pair. These calls a bit shorter that the typical call of this species, which I believe indicates some level of agitation. A couple of more typical calls at 26-29s	A	Andrew Spencer	[]	2009-02-15 00:00:00
14	314303		HETH	—	2016	04-29	19:45	call	Great Pubnico Lake Road, Canada	10 m	43.7	-65.8	Great Pubnico Lake Road, Canada		B	Alix dEntremont	[]	2016-04-29 00:00:00
2	127012		SPTO	megalonyx	2001	05-13	07:10	call	San Diego County, United States	100 m	32.7	-117.0	Sweetwater River, SDNWR, San Diego County, United States	calls of a pair;	B	Richard E. Webster	['CALT', 'COYE', 'YBCH', 'American Yellow Warbler']	2001-05-13 00:00:00
6	293823		EATO	—	2015	11-23	09:30	call	Alabama, United States	60 m	32.0	-85.1	Eufaula, Barbour County, Alabama, United States		A	Jim Holmes	[]	2015-11-23 00:00:00

XXX Attic

# BEWR HOWR PAWR
# BGGN
# CATH
# NOMO
# LEGO
# BRCR
# RBNU WBNU PYNU
# OATI CBCH
# HUVI WAVI CAVI
# WEKI
# BLPH SAPH WEWP OSFL ATFL PSFL
# RBSA
# HAWO DOWO NUWO ACWO PIWO NOFL
# RTHA(age) RSHA COHA SSHA AMKE MERL
# COLO

# WCSP
#   - Around bay area?
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'WCSP',
        df.state.str.lower().str.match(r'.*\b(ca|california)\b.*'),
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    .sort_values(['subspecies', 'lat'], ascending=[True, False])
    # .sort_values(['lat'], ascending=False)
    # .sort_values(['lng'], ascending=True)
    # View
    .pipe(_recs_show,
        # scale=3,
    )
    [:500]
)

# SOSP
#   - Don't know what to do with this one
#   - By subspecies?
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'SOSP',
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    [lambda df: ~df.subspecies.isin([''])]
    .sample(100, random_state=0)
    .sort_values(['subspecies'], ascending=[True])
    # View
    .pipe(_recs_show,
        # scale=3,
    )
    [:100]
)

# BEWR
#   - Don't know what to do with this one
#   - Around US?
#   - Across individuals?
(search_recs
    .pipe(df_inspect, lambda df: (df.shape,))
    # Filter
    [lambda df: reduce(lambda x, y: x & y, [
        df.species == 'BEWR',
    ])]
    .pipe(df_inspect, lambda df: (df.shape,))
    # Sort
    .sample(100, random_state=0)
    .sort_values(['lng'], ascending=[True])
    # View
    .pipe(_recs_show,
        # scale=3,
    )
    [:100]
)

Try python-pptx

import pptx
path = os.path.expanduser('~/Desktop/Birds.pptx')
prs = pptx.Presentation(path)

dirs(prs)

{
  'core_properties': <pptx.parts.coreprops.CorePropertiesPart object at 0x188438eb8>,
  'element': <Element {http://schemas.openxmlformats.org/presentationml/2006/main}presentation at 0x188415a48>,
  'notes_master': <pptx.slide.NotesMaster object at 0x1886e07c8>,
  'part': <pptx.parts.presentation.PresentationPart object at 0x188438208>,
  'save': <bound method Presentation.save of <pptx.presentation.Presentation object at 0x1074ce5e8>>,
  'slide_height': pptx.util.Emu(6858000),
  'slide_layouts': <pptx.slide.SlideLayouts object at 0x187ea3688>,
  'slide_master': <pptx.slide.SlideMaster object at 0x1886ffe60>,
  'slide_masters': <pptx.slide.SlideMasters object at 0x188719508>,
  'slide_width': pptx.util.Emu(12192000),
  'slides': <pptx.slide.Slides object at 0x18889a6c0>
}

dirs(prs.slides)

{
  'add_slide': <bound method Slides.add_slide of <pptx.slide.Slides object at 0x18889a6c0>>,
  'element': <Element {http://schemas.openxmlformats.org/presentationml/2006/main}sldIdLst at 0x1886f7d68>,
  'get': <bound method Slides.get of <pptx.slide.Slides object at 0x18889a6c0>>,
  'index': <bound method Slides.index of <pptx.slide.Slides object at 0x18889a6c0>>,
  'parent': <pptx.presentation.Presentation object at 0x1074ce5e8>,
  'part': <pptx.parts.presentation.PresentationPart object at 0x188438208>
}

list(prs.slides)

[
  <pptx.slide.Slide object at 0x188e16ea8>,
  <pptx.slide.Slide object at 0x1887029a8>,
  <pptx.slide.Slide object at 0x1092dcf98>,
  <pptx.slide.Slide object at 0x188e16f48>,
  <pptx.slide.Slide object at 0x188e16ef8>,
  <pptx.slide.Slide object at 0x188e16a48>,
  <pptx.slide.Slide object at 0x188e16e58>,
  <pptx.slide.Slide object at 0x188e16138>,
  <pptx.slide.Slide object at 0x188e16c78>,
  <pptx.slide.Slide object at 0x188e16e08>,
  <pptx.slide.Slide object at 0x188e16cc8>,
  <pptx.slide.Slide object at 0x188e16db8>,
  <pptx.slide.Slide object at 0x188e16c28>,
  <pptx.slide.Slide object at 0x188e16b38>,
  <pptx.slide.Slide object at 0x188e16778>
]

dirs(prs.slides[5])

{
  'background': <pptx.slide._Background object at 0x18435f288>,
  'element': <Element {http://schemas.openxmlformats.org/presentationml/2006/main}sld at 0x188710728>,
  'follow_master_background': True,
  'has_notes_slide': True,
  'name': '',
  'notes_slide': <pptx.slide.NotesSlide object at 0x186600318>,
  'part': <pptx.parts.slide.SlidePart object at 0x18872ba20>,
  'placeholders': <pptx.shapes.shapetree.SlidePlaceholders object at 0x18985d4e0>,
  'shapes': <pptx.shapes.shapetree.SlideShapes object at 0x17f51b5e8>,
  'slide_id': 263,
  'slide_layout': <pptx.slide.SlideLayout object at 0x1897736d8>
}

list(prs.slides[5].shapes)

[<pptx.shapes.picture.Picture object at 0x189791588>]

list(prs.slides[-1].shapes)

[
  <pptx.shapes.placeholder.SlidePlaceholder object at 0x189791b38>,
  <pptx.shapes.picture.Picture object at 0x189791ba8>,
  <pptx.shapes.picture.Picture object at 0x18986c5c0>
]

dir(prs.slides[-1].shapes[0])

[
  '__class__',
  '__delattr__',
  '__dict__',
  '__dir__',
  '__doc__',
  '__eq__',
  '__format__',
  '__ge__',
  '__getattribute__',
  '__gt__',
  '__hash__',
  '__init__',
  '__init_subclass__',
  '__le__',
  '__lt__',
  '__module__',
  '__ne__',
  '__new__',
  '__reduce__',
  '__reduce_ex__',
  '__repr__',
  '__setattr__',
  '__sizeof__',
  '__str__',
  '__subclasshook__',
  '__weakref__',
  '_base_placeholder',
  '_effective_value',
  '_element',
  '_inherited_value',
  '_parent',
  '_replace_placeholder_with',
  '_sp',
  'adjustments',
  'auto_shape_type',
  'click_action',
  'element',
  'fill',
  'get_or_add_ln',
  'has_chart',
  'has_table',
  'has_text_frame',
  'height',
  'is_placeholder',
  'left',
  'line',
  'ln',
  'name',
  'part',
  'placeholder_format',
  'rotation',
  'shadow',
  'shape_id',
  'shape_type',
  'text',
  'text_frame',
  'top',
  'width'
]

prs.slides[-1].shapes[0].text

'How to audio+image'

dir(prs.slides[-1].shapes[1])

[
  '__class__',
  '__delattr__',
  '__dict__',
  '__dir__',
  '__doc__',
  '__eq__',
  '__format__',
  '__ge__',
  '__getattribute__',
  '__gt__',
  '__hash__',
  '__init__',
  '__init_subclass__',
  '__le__',
  '__lt__',
  '__module__',
  '__ne__',
  '__new__',
  '__reduce__',
  '__reduce_ex__',
  '__repr__',
  '__setattr__',
  '__sizeof__',
  '__str__',
  '__subclasshook__',
  '__weakref__',
  '_element',
  '_parent',
  '_pic',
  'auto_shape_type',
  'click_action',
  'crop_bottom',
  'crop_left',
  'crop_right',
  'crop_top',
  'element',
  'get_or_add_ln',
  'has_chart',
  'has_table',
  'has_text_frame',
  'height',
  'image',
  'is_placeholder',
  'left',
  'line',
  'ln',
  'name',
  'part',
  'placeholder_format',
  'rotation',
  'shadow',
  'shape_id',
  'shape_type',
  'top',
  'width'
]

(prs.slides[-1].shapes[1].shape_type, pptx.enum.shapes.MSO_SHAPE_TYPE.PICTURE)

(pptx.enum.base.EnumValue(13), pptx.enum.base.EnumValue(13))

dirs(pptx.enum.shapes.MSO_SHAPE_TYPE)

{
  'AUTO_SHAPE': pptx.enum.base.EnumValue(1),
  'CALLOUT': pptx.enum.base.EnumValue(2),
  'CANVAS': pptx.enum.base.EnumValue(20),
  'CHART': pptx.enum.base.EnumValue(3),
  'COMMENT': pptx.enum.base.EnumValue(4),
  'DIAGRAM': pptx.enum.base.EnumValue(21),
  'EMBEDDED_OLE_OBJECT': pptx.enum.base.EnumValue(7),
  'FORM_CONTROL': pptx.enum.base.EnumValue(8),
  'FREEFORM': pptx.enum.base.EnumValue(5),
  'GROUP': pptx.enum.base.EnumValue(6),
  'IGX_GRAPHIC': pptx.enum.base.EnumValue(24),
  'INK': pptx.enum.base.EnumValue(22),
  'INK_COMMENT': pptx.enum.base.EnumValue(23),
  'LINE': pptx.enum.base.EnumValue(9),
  'LINKED_OLE_OBJECT': pptx.enum.base.EnumValue(10),
  'LINKED_PICTURE': pptx.enum.base.EnumValue(11),
  'MEDIA': pptx.enum.base.EnumValue(16),
  'MIXED': pptx.enum.base.EnumValue(-2),
  'OLE_CONTROL_OBJECT': pptx.enum.base.EnumValue(12),
  'PICTURE': pptx.enum.base.EnumValue(13),
  'PLACEHOLDER': pptx.enum.base.EnumValue(14),
  'SCRIPT_ANCHOR': pptx.enum.base.EnumValue(18),
  'TABLE': pptx.enum.base.EnumValue(19),
  'TEXT_BOX': pptx.enum.base.EnumValue(17),
  'TEXT_EFFECT': pptx.enum.base.EnumValue(15),
  'WEB_VIDEO': pptx.enum.base.EnumValue(26),
  'validate': <bound method EnumerationBase.validate of <class 'pptx.enum.shapes.MSO_SHAPE_TYPE'>>
}

dirs(pptx.enum.shapes.PP_MEDIA_TYPE)

{
  'MIXED': pptx.enum.base.EnumValue(-2),
  'MOVIE': pptx.enum.base.EnumValue(3),
  'OTHER': pptx.enum.base.EnumValue(1),
  'SOUND': pptx.enum.base.EnumValue(1),
  'validate': <bound method EnumerationBase.validate of <class 'pptx.enum.shapes.PP_MEDIA_TYPE'>>
}

dir(prs.slides[-1].shapes[1].click_action)

[
  '__class__',
  '__delattr__',
  '__dict__',
  '__dir__',
  '__doc__',
  '__eq__',
  '__format__',
  '__ge__',
  '__getattribute__',
  '__gt__',
  '__hash__',
  '__init__',
  '__init_subclass__',
  '__le__',
  '__lt__',
  '__module__',
  '__ne__',
  '__new__',
  '__reduce__',
  '__reduce_ex__',
  '__repr__',
  '__setattr__',
  '__sizeof__',
  '__str__',
  '__subclasshook__',
  '__weakref__',
  '_clear_click_action',
  '_element',
  '_hlink',
  '_hover',
  '_parent',
  '_slide',
  '_slide_index',
  '_slides',
  'action',
  'hyperlink',
  'part',
  'target_slide'
]

dir(prs.slides[-1].shapes)

[
  '__class__',
  '__delattr__',
  '__dict__',
  '__dir__',
  '__doc__',
  '__eq__',
  '__format__',
  '__ge__',
  '__getattribute__',
  '__getitem__',
  '__gt__',
  '__hash__',
  '__init__',
  '__init_subclass__',
  '__iter__',
  '__le__',
  '__len__',
  '__lt__',
  '__module__',
  '__ne__',
  '__new__',
  '__reduce__',
  '__reduce_ex__',
  '__repr__',
  '__setattr__',
  '__sizeof__',
  '__slots__',
  '__str__',
  '__subclasshook__',
  '__weakref__',
  '_add_chart_graphicFrame',
  '_add_cxnSp',
  '_add_graphicFrame_containing_table',
  '_add_pic_from_image_part',
  '_add_sp',
  '_add_textbox_sp',
  '_add_video_timing',
  '_cached_max_shape_id',
  '_element',
  '_grpSp',
  '_is_member_elm',
  '_iter_member_elms',
  '_next_ph_name',
  '_next_shape_id',
  '_parent',
  '_recalculate_extents',
  '_shape_factory',
  '_spTree',
  'add_chart',
  'add_connector',
  'add_group_shape',
  'add_movie',
  'add_picture',
  'add_shape',
  'add_table',
  'add_textbox',
  'build_freeform',
  'clone_layout_placeholders',
  'clone_placeholder',
  'element',
  'index',
  'parent',
  'part',
  'ph_basename',
  'placeholders',
  'title',
  'turbo_add_enabled'
]